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-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-#
-# DPD Computation Engine main file.
-#
-# http://www.opendigitalradio.org
-# Licence: The MIT License, see notice at the end of this file
-
-"""This Python script is the main file for ODR-DabMod's DPD Computation Engine.
-This engine calculates and updates the parameter of the digital
-predistortion module of ODR-DabMod."""
-
-import sys
-import datetime
-import os
-import argparse
-import matplotlib
-
-matplotlib.use('Agg')
-
-parser = argparse.ArgumentParser(
- description="DPD Computation Engine for ODR-DabMod")
-parser.add_argument('--port', default=50055, type=int,
- help='port of DPD server to connect to (default: 50055)',
- required=False)
-parser.add_argument('--rc-port', default=9400, type=int,
- help='port of ODR-DabMod ZMQ Remote Control to connect to (default: 9400)',
- required=False)
-parser.add_argument('--samplerate', default=8192000, type=int,
- help='Sample rate',
- required=False)
-parser.add_argument('--coefs', default='poly.coef',
- help='File with DPD coefficients, which will be read by ODR-DabMod',
- required=False)
-parser.add_argument('--txgain', default=-1,
- help='TX Gain, -1 to leave unchanged',
- required=False,
- type=int)
-parser.add_argument('--rxgain', default=30,
- help='TX Gain, -1 to leave unchanged',
- required=False,
- type=int)
-parser.add_argument('--digital_gain', default=0.01,
- help='Digital Gain',
- required=False,
- type=float)
-parser.add_argument('--target_median', default=0.05,
- help='The target median for the RX and TX AGC',
- required=False,
- type=float)
-parser.add_argument('--samps', default='81920', type=int,
- help='Number of samples to request from ODR-DabMod',
- required=False)
-parser.add_argument('-i', '--iterations', default=10, type=int,
- help='Number of iterations to run',
- required=False)
-parser.add_argument('-L', '--lut',
- help='Use lookup table instead of polynomial predistorter',
- action="store_true")
-parser.add_argument('--enable-txgain-agc',
- help='Enable the TX gain AGC',
- action="store_true")
-parser.add_argument('--plot',
- help='Enable all plots, to be more selective choose plots in GlobalConfig.py',
- action="store_true")
-parser.add_argument('--name', default="", type=str,
- help='Name of the logging directory')
-parser.add_argument('-r', '--reset', action="store_true",
- help='Reset the DPD settings to the defaults.')
-parser.add_argument('-s', '--status', action="store_true",
- help='Display the currently running DPD settings.')
-parser.add_argument('--measure', action="store_true",
- help='Only measure metrics once')
-
-cli_args = parser.parse_args()
-
-port = cli_args.port
-port_rc = cli_args.rc_port
-coef_path = cli_args.coefs
-digital_gain = cli_args.digital_gain
-num_iter = cli_args.iterations
-rxgain = cli_args.rxgain
-txgain = cli_args.txgain
-name = cli_args.name
-plot = cli_args.plot
-
-# Logging
-import logging
-
-# Simple usage scenarios don't need to clutter /tmp
-if not (cli_args.status or cli_args.reset or cli_args.measure):
- dt = datetime.datetime.now().isoformat()
- logging_path = '/tmp/dpd_{}'.format(dt).replace('.', '_').replace(':', '-')
- if name:
- logging_path += '_' + name
- print("Logs and plots written to {}".format(logging_path))
- os.makedirs(logging_path)
- logging.basicConfig(format='%(asctime)s - %(module)s - %(levelname)s - %(message)s',
- datefmt='%Y-%m-%d %H:%M:%S',
- filename='{}/dpd.log'.format(logging_path),
- filemode='w',
- level=logging.DEBUG)
- # also log up to INFO to console
- console = logging.StreamHandler()
- console.setLevel(logging.INFO)
- # set a format which is simpler for console use
- formatter = logging.Formatter('%(asctime)s - %(module)s - %(levelname)s - %(message)s')
- # tell the handler to use this format
- console.setFormatter(formatter)
- # add the handler to the root logger
- logging.getLogger('').addHandler(console)
-else:
- dt = datetime.datetime.now().isoformat()
- logging.basicConfig(format='%(asctime)s - %(module)s - %(levelname)s - %(message)s',
- datefmt='%Y-%m-%d %H:%M:%S',
- level=logging.INFO)
- logging_path = None
-
-logging.info("DPDCE starting up with options: {}".format(cli_args))
-
-import numpy as np
-import traceback
-from src.Model import Lut, Poly
-import src.Heuristics as Heuristics
-from src.Measure import Measure
-from src.ExtractStatistic import ExtractStatistic
-from src.Adapt import Adapt, dpddata_to_str
-from src.RX_Agc import Agc
-from src.TX_Agc import TX_Agc
-from src.Symbol_align import Symbol_align
-from src.GlobalConfig import GlobalConfig
-from src.MER import MER
-from src.Measure_Shoulders import Measure_Shoulders
-
-c = GlobalConfig(cli_args, logging_path)
-SA = Symbol_align(c)
-MER = MER(c)
-MS = Measure_Shoulders(c)
-meas = Measure(c, cli_args.samplerate, port, cli_args.samps)
-extStat = ExtractStatistic(c)
-adapt = Adapt(c, port_rc, coef_path)
-
-if cli_args.status:
- txgain = adapt.get_txgain()
- rxgain = adapt.get_rxgain()
- digital_gain = adapt.get_digital_gain()
- dpddata = dpddata_to_str(adapt.get_predistorter())
-
- logging.info("ODR-DabMod currently running with TXGain {}, RXGain {}, digital gain {} and {}".format(
- txgain, rxgain, digital_gain, dpddata))
- sys.exit(0)
-
-if cli_args.lut:
- model = Lut(c)
-else:
- model = Poly(c)
-
-# Models have the default settings on startup
-adapt.set_predistorter(model.get_dpd_data())
-adapt.set_digital_gain(digital_gain)
-
-# Set RX Gain
-if rxgain == -1:
- rxgain = adapt.get_rxgain()
-else:
- adapt.set_rxgain(rxgain)
-
-# Set TX Gain
-if txgain == -1:
- txgain = adapt.get_txgain()
-else:
- adapt.set_txgain(txgain)
-
-tx_gain = adapt.get_txgain()
-rx_gain = adapt.get_rxgain()
-digital_gain = adapt.get_digital_gain()
-
-dpddata = adapt.get_predistorter()
-
-logging.info("TX gain {}, RX gain {}, digital_gain {}, {!s}".format(
- tx_gain, rx_gain, digital_gain, dpddata_to_str(dpddata)))
-
-if cli_args.reset:
- logging.info("DPD Settings were reset to default values.")
- sys.exit(0)
-
-# Automatic Gain Control
-agc = Agc(meas, adapt, c)
-agc.run()
-
-if cli_args.measure:
- txframe_aligned, tx_ts, rxframe_aligned, rx_ts, rx_median = meas.get_samples()
-
- print("TX signal median {}".format(np.median(np.abs(txframe_aligned))))
- print("RX signal median {}".format(rx_median))
-
- tx, rx, phase_diff, n_per_bin = extStat.extract(txframe_aligned, rxframe_aligned)
-
- off = SA.calc_offset(txframe_aligned)
- print("off {}".format(off))
- tx_mer = MER.calc_mer(txframe_aligned[off:off + c.T_U], debug_name='TX')
- print("tx_mer {}".format(tx_mer))
- rx_mer = MER.calc_mer(rxframe_aligned[off:off + c.T_U], debug_name='RX')
- print("rx_mer {}".format(rx_mer))
-
- mse = np.mean(np.abs((txframe_aligned - rxframe_aligned) ** 2))
- print("mse {}".format(mse))
-
- digital_gain = adapt.get_digital_gain()
- print("digital_gain {}".format(digital_gain))
-
- #rx_shoulder_tuple = MS.average_shoulders(rxframe_aligned)
- #tx_shoulder_tuple = MS.average_shoulders(txframe_aligned)
- sys.exit(0)
-
-# Disable TXGain AGC by default, so that the experiments are controlled
-# better.
-tx_agc = None
-if cli_args.enable_txgain_agc:
- tx_agc = TX_Agc(adapt, c)
-
-state = 'report'
-i = 0
-lr = None
-n_meas = None
-while i < num_iter:
- try:
- # Measure
- if state == 'measure':
- # Get Samples and check gain
- txframe_aligned, tx_ts, rxframe_aligned, rx_ts, rx_median = meas.get_samples()
- if tx_agc and tx_agc.adapt_if_necessary(txframe_aligned):
- continue
-
- # Extract usable data from measurement
- tx, rx, phase_diff, n_per_bin = extStat.extract(txframe_aligned, rxframe_aligned)
-
- n_meas = Heuristics.get_n_meas(i)
- if extStat.n_meas >= n_meas: # Use as many measurements nr of runs
- state = 'model'
- else:
- state = 'measure'
-
- # Model
- elif state == 'model':
- # Calculate new model parameters and delete old measurements
- if any([x is None for x in [tx, rx, phase_diff]]):
- logging.error("No data to calculate model")
- state = 'measure'
- continue
-
- lr = Heuristics.get_learning_rate(i)
- model.train(tx, rx, phase_diff, lr=lr)
- dpddata = model.get_dpd_data()
- extStat = ExtractStatistic(c)
- state = 'adapt'
-
- # Adapt
- elif state == 'adapt':
- adapt.set_predistorter(dpddata)
- state = 'report'
-
- # Report
- elif state == 'report':
- try:
- txframe_aligned, tx_ts, rxframe_aligned, rx_ts, rx_median = meas.get_samples()
-
- # Store all settings for pre-distortion, tx and rx
- adapt.dump()
-
- # Collect logging data
- off = SA.calc_offset(txframe_aligned)
- tx_mer = MER.calc_mer(txframe_aligned[off:off + c.T_U], debug_name='TX')
- rx_mer = MER.calc_mer(rxframe_aligned[off:off + c.T_U], debug_name='RX')
- mse = np.mean(np.abs((txframe_aligned - rxframe_aligned) ** 2))
- tx_gain = adapt.get_txgain()
- rx_gain = adapt.get_rxgain()
- digital_gain = adapt.get_digital_gain()
- tx_median = np.median(np.abs(txframe_aligned))
- rx_shoulder_tuple = MS.average_shoulders(rxframe_aligned)
- tx_shoulder_tuple = MS.average_shoulders(txframe_aligned)
-
- # Generic logging
- logging.info(list((name, eval(name)) for name in
- ['i', 'tx_mer', 'tx_shoulder_tuple', 'rx_mer',
- 'rx_shoulder_tuple', 'mse', 'tx_gain',
- 'digital_gain', 'rx_gain', 'rx_median',
- 'tx_median', 'lr', 'n_meas']))
-
- # Model specific logging
- if dpddata[0] == 'poly':
- coefs_am = dpddata[1]
- coefs_pm = dpddata[2]
- logging.info('It {}: coefs_am {}'.
- format(i, coefs_am))
- logging.info('It {}: coefs_pm {}'.
- format(i, coefs_pm))
- elif dpddata[0] == 'lut':
- scalefactor = dpddata[1]
- lut = dpddata[2]
- logging.info('It {}: LUT scalefactor {}, LUT {}'.
- format(i, scalefactor, lut))
- except:
- logging.error('Iteration {}: Report failed.'.format(i))
- logging.error(traceback.format_exc())
- i += 1
- state = 'measure'
-
- except:
- logging.error('Iteration {} failed.'.format(i))
- logging.error(traceback.format_exc())
- i += 1
- state = 'measure'
-
-# The MIT License (MIT)
-#
-# Copyright (c) 2017 Andreas Steger
-# Copyright (c) 2017 Matthias P. Braendli
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
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